# Revised from :
# https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html

"""
===================================================
Faces recognition example using eigenfaces and SVMs
===================================================

The dataset used in this example is a preprocessed excerpt of the
"Labeled Faces in the Wild", aka LFW_:

  http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)

.. _LFW: http://vis-www.cs.umass.edu/lfw/

Expected results for the top 7 most represented people in the dataset:

================== ============ ======= ========== =======
                   precision    recall  f1-score   support
================== ============ ======= ========== =======
     Ariel Sharon       0.67      0.92      0.77        13
     Colin Powell       0.75      0.78      0.76        60
  Donald Rumsfeld       0.78      0.67      0.72        27
    George W Bush       0.86      0.86      0.86       146
Gerhard Schroeder       0.76      0.76      0.76        25
      Hugo Chavez       0.67      0.67      0.67        15
       Tony Blair       0.81      0.69      0.75        36

      avg / total       0.80      0.80      0.80       322
================== ============ ======= ========== =======

"""
from time import time
import logging
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import fetch_lfw_people
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.decomposition import PCA
from sklearn.svm import SVC

import ssl
ssl._create_default_https_context = ssl._create_unverified_context

# #############################################################################
# Download the data, if not already on disk and load it as numpy arrays
''' URL: https://pan.baidu.com/s/1ccBlZxJDx_8z_2u3kv0oaA
    code: 1220
'''
#faces = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
faces = fetch_lfw_people(data_home="/Users/haojiash/Downloads/", min_faces_per_person=70, resize=0.4, download_if_missing=False)

# introspect the images arrays to find the shapes (for plotting)
n_samples, h, w = faces.images.shape

# for machine learning we use the 2 data directly (as relative pixel
# positions info is ignored by this model)
X = faces.data
n_features = X.shape[1]

# the label to predict is the id of the person
y = faces.target
target_names = faces.target_names
n_classes = target_names.shape[0]

print("Total dataset size:")
print("n_samples: %d" % n_samples)
print("n_features: %d" % n_features)
print("n_classes: %d" % n_classes)


# #############################################################################
# Split into a training set and a test set using a stratified k fold

# split into a training and testing set
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, random_state=42)


# #############################################################################
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 0.90
#n_components = 75

print("Extracting the top %0.2f eigenfaces from %d faces"
      % (n_components, X_train.shape[0]))
t0 = time()

pca = PCA(n_components=n_components, whiten=True).fit(X_train)
print("Top %d components extracted in %0.3fs" % (pca.components_.shape[0], time() - t0))

eigenfaces = pca.components_.reshape((pca.components_.shape[0], h, w))

# calculate the maximum of the dot product of every two eigen faces.
i = 0
e = 0
while i < eigenfaces.shape[0] - 1:
  j = i + 1
  while j <= eigenfaces.shape[0] - 1:
    ee = eigenfaces[i].reshape((-1,1)).T.dot(eigenfaces[j].reshape((-1,1)))
    if ee > e:
      e = ee
    j +=1
  i += 1

print(e)

print("Projecting the input data on the eigenfaces orthonormal basis")
t0 = time()

X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done in %0.3fs" % (time() - t0))


# #############################################################################
# Train a SVM classification model

print("Fitting the classifier to the training set")
t0 = time()

param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
              'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
                   param_grid, cv=5)
clf = clf.fit(X_train_pca, y_train)

print("done in %0.3fs" % (time() - t0))
print("Best estimator found by grid search:")
print(clf.best_estimator_)


# #############################################################################
# Quantitative evaluation of the model quality on the test set

print("Predicting %d people's names on the test set" % (X_test_pca.shape[0]))
t0 = time()
y_pred = clf.predict(X_test_pca)
print("done in %0.3fs" % (time() - t0))

print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))


# #############################################################################
# Qualitative evaluation of the predictions using matplotlib

def plot_gallery(images, titles, h, w, n_row=3, n_col=8):
    """Helper function to plot a gallery of portraits"""
    plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))
    plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)
    for i in range(n_row * n_col):
        plt.subplot(n_row, n_col, i + 1)
        plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray)
        plt.title(titles[i], size=12)
        plt.xticks(())
        plt.yticks(())


# plot the result of the prediction on a portion of the test set

def title(y_pred, y_test, target_names, i):
    pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1]
    true_name = target_names[y_test[i]].rsplit(' ', 1)[-1]
    return 'predicted: %s\ntrue:      %s' % (pred_name, true_name)

prediction_titles = [title(y_pred, y_test, target_names, i)
                     for i in range(y_pred.shape[0])]

plot_gallery(X_test, prediction_titles, h, w)

# plot the gallery of the most significative eigenfaces

eigenface_titles = ["eigenface %d(%d)" % (i, n_components) for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w)

# plot the ``mean face'' image
fig = plt.figure()
plt.imshow(pca.mean_.reshape(h, w), cmap=plt.cm.bone)
plt.title('The mean face')

plt.show()
